An optimum multilayer perceptron neural receiver for signal detection
- 1 December 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 1 (4) , 298-300
- https://doi.org/10.1109/72.80267
Abstract
The M-input optimum likelihood-ratio receiver is generalized by considering the case of different signal amplitudes on the receiver primary input lines. Using the more general likelihood-ratio receiver as a reference, an equivalent optimum multilayer perceptron neural network (or neural receiver) is identified for detecting the presence of an M-dimensional target signal corrupted by bandlimited white Gaussian noise. Analytical results are supported by Monte Carlo simulation runs which indicate that the detection capability of the proposed neural receiver is not sensitive to the level of training or number of patterns in the training set.<>Keywords
This publication has 5 references indexed in Scilit:
- Survey of neural network technology for automatic target recognitionIEEE Transactions on Neural Networks, 1990
- A comparison of a nearest neighbor classifier and a neural network for numeric handprint character recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Target detection using a neural network based passive sonar systemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Application of neural networks to terrain classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Neural networks for extraction of weak targets in high clutter environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989